E2B Alternatives for Self-Hosted AI Sandboxes 2026

The main E2B alternatives for AI agent sandboxes in 2026 are Modal, Daytona, Morph Cloud, Temps, and building directly on Firecracker or gVisor. There is no single winner: the choice collapses to two questions, which are whether you can run your own Linux host and whether your code is trusted.

TL;DR: E2B provides polished managed sandboxes, billed per second on vCPU plus RAM (about $0.0000303/s for its default 2 vCPU / 512 MiB sandbox). If you want to stay managed, E2B is still the strongest general option, Modal is better for Python and GPU workloads, and Morph Cloud is better if you need to snapshot and fork agent state. If per-second billing or data residency is the problem, the self-hosted options are Temps and Daytona, though Daytona's open-source repo has been unmaintained since June 2026, and Temps is the one that also offers Firecracker microVM isolation. Costs diverge sharply at volume: sandbox compute that runs ~$545/mo on E2B runs on a ~$6/mo VPS self-hosted.

Who wrote this: I build Temps, including the Firecracker sandbox backend described below, so the implementation details here come from having written them rather than from a vendor datasheet. That also means I have an interest in one of these tools, which is why the next paragraph exists.

Disclosure: Temps publishes this blog, so treat our section as a vendor describing its own product. We have tried to describe every other tool the way its own documentation does, and to be specific about the cases where E2B, Modal, or Morph Cloud is the better choice. Pricing figures are as published at the time of writing and move often, so check each vendor's current pricing page before you commit to anything.

What Are the Best E2B Alternatives in 2026?

The first question is whether you can run your own Linux host. If you cannot, or do not want to, the answer is a managed sandbox and E2B is a strong default. If you can, self-hosting removes per-compute-second billing entirely and keeps code on your infrastructure, at the cost of operating a server. The second question, whether the code you execute is trusted, decides how much isolation you need on top.

The honest summary: E2B is hard to beat if managed is acceptable to you. Its SDK is more mature than anything in the self-hosted category, and the one-time $100 credit covers a lot of prototyping. The self-hosted options become compelling at two specific thresholds, which are when your sandbox bill outgrows a VPS, and when your compliance posture will not allow code execution in someone else's cloud.

E2B Alternatives at a Glance

Temps vs E2B: Head-to-Head

Since Temps is our product, here is the comparison including the places E2B wins, not just the places we do.

Read that top block honestly: if you are prototyping, E2B gets you running today and Temps does not. The self-hosted case is about what happens at volume and under compliance constraints, not about being easier to start with.

Why Are Teams Looking for E2B Alternatives?

E2B launched in 2023 and quickly became the default sandbox layer for AI coding agents, powering integrations with OpenAI Assistants, LangChain, and dozens of agent frameworks. Its Firecracker-based microVMs boot in roughly 150–200 ms and offer genuine kernel-level isolation.

But as production AI workloads scale, three friction points drive teams toward alternatives:

Cost at Scale

E2B bills per second, charging vCPU and RAM separately: $0.000028/s for the default 2 vCPU, plus $0.0000045 per GiB per second. A default sandbox therefore costs about $0.0000303/s. An agent running 10 minutes of execution per task across 1,000 daily tasks generates 18 million compute-seconds a month, or roughly $545 for sandbox compute alone. Add network egress and storage, and the bill grows. A $6/mo Hetzner VPS running Temps handles the same workload with no per-execution fees.

Data Residency

Every code snippet, file read, and environment variable passed to an E2B sandbox transits E2B's cloud. For teams under SOC 2, HIPAA, or EU data-residency requirements, that is a blocker. Self-hosted alternatives keep all execution on the team's own servers. Temps documents exactly what stays on your machine in its data ownership and privacy guide.

Single-Toolchain Constraint

E2B is a sandbox layer — not a deployment platform, not an observability stack, not an AI gateway. Teams running agents in production still need error tracking, request logging, LLM cost monitoring, and a deployment pipeline. Every additional SaaS tool adds a billing line and a data-sharing agreement.

1. E2B — The Baseline You're Comparing Against

E2B uses Firecracker microVMs to provide genuine kernel-level isolation for each sandbox. Firecracker was originally built by AWS to power Lambda and Fargate, and E2B's implementation boots new VMs in roughly 150–200 ms — fast enough for interactive agent workflows.

Citation Capsule: E2B's Code Interpreter SDK is used by major AI frameworks including LangChain, LlamaIndex, and OpenAI's Assistants. E2B sandboxes run on Firecracker microVMs developed by AWS, providing full kernel isolation and a fresh Linux environment per sandbox. Pricing is per compute-second.

Why E2B for AI Sandboxes?

E2B's managed model removes all server operations. Call the API, get a sandbox, run code, get the output. The SDK integrates with every major AI framework:

from e2b_code_interpreter import Sandbox

with Sandbox() as sandbox:
    execution = sandbox.run_code("import pandas as pd; df = pd.DataFrame({'a': [1, 2, 3]}); print(df)")
    print(execution.text)

The Hobby tier gives a one-time $100 in credits, with sessions capped at 1 hour and 20 concurrent sandboxes. Beyond that, billing is usage-based, and there is a Pro plan at $150/mo plus usage.

Pros

• Firecracker microVMs — genuine kernel-level isolation
• ~150–200 ms boot times
• Managed infrastructure — no server operations
• SDK integrations with LangChain, OpenAI, LlamaIndex, CrewAI
• One-time $100 in free credits for new accounts
• Filesystem, networking, and process isolation per sandbox

Cons

• Per-second billing scales poorly at high execution volume, and the Pro plan adds $150/mo before usage
• All code execution transits E2B's cloud — data residency concerns
• No built-in AI Gateway, error tracking, or deployment platform
• Self-hosting exists but is a Terraform + Nomad cluster deployment, not a single binary

Best For

Teams prototyping AI agents, early-stage products needing zero infrastructure, and workloads requiring strict kernel-level isolation for untrusted user-submitted code.

2. Temps — Self-Hosted, With a Choice of Isolation

This is our product, so weigh it accordingly. The specifics below are checkable against the source, and the limitations section is not decorative.

Temps does not force a single isolation model. It ships two sandbox backends behind one API: Docker containers with isolated networking and ephemeral filesystems, and Firecracker microVMs running on KVM. For most AI agent workloads (running Python scripts, executing shell commands, reading and writing files), Docker isolation is sufficient and boots in sub-second time from pre-pulled images. When the threat model calls for a hardware-virtualized boundary, the same sandbox request runs as a microVM instead.

The specific claim: among the self-hosted options here, Temps is the one that offers Firecracker microVM isolation as a supported backend, chosen per sandbox, alongside an AI Gateway, a Sentry-compatible error tracker, and a deployment platform in a single Apache 2.0 binary. That combination is the argument for it. It is not the fastest way to get a sandbox running, and on a host without KVM the microVM half of that claim does not apply.

How Temps Works as an AI Sandbox

When you run the AI Autofixer, Temps clones the production container into an isolated sibling container, applies the code fix inside that sandbox, runs tests, and destroys the sandbox. The pattern is reusable for any agent workload:

// POST /api/v1/sandboxes/{id}/exec — runs inside the sandbox, which has
// isolated networking, an ephemeral filesystem, and a clean environment
const res = await fetch(
  `https://your-temps-instance.com/api/v1/sandboxes/${sandboxId}/exec`,
  {
    method: "POST",
    headers: { Authorization: `Bearer ${process.env.TEMPS_API_KEY}` },
    body: JSON.stringify({
      cmd: ["python", "main.py"],
      env: { OPENAI_API_KEY: process.env.OPENAI_API_KEY },
      cwd: "/workspace",
    }),
  },
);

const { stdout, stderr, exit_code } = await res.json();

The same thing from the CLI, which is often what you actually reach for while debugging an agent:

bunx @temps-sdk/cli sandbox exec <sandbox-id> -- python main.py

Secrets reach the sandbox through the platform rather than through your prompt, which the agent sandbox secrets documentation covers in detail.

The AI Gateway routes LLM calls across providers through a single OpenAI-compatible endpoint:

import OpenAI from "openai";

// Replace api.openai.com with your Temps AI Gateway
const client = new OpenAI({
  baseURL: "https://your-temps-instance.com/api/ai-gateway/v1",
  apiKey: process.env.TEMPS_API_KEY,
});

const response = await client.chat.completions.create({
  model: "anthropic/claude-sonnet-4-6", // or openai/gpt-4o, google/gemini-2.0-flash
  messages: [{ role: "user", content: "Fix this bug: ..." }],
});

Does Temps Support Firecracker microVMs?

Yes. Temps runs Firecracker microVMs as a first-class sandbox backend alongside Docker, and you pick the backend per sandbox. A Firecracker sandbox is a real KVM virtual machine with a pinned guest kernel and its own ext4 root filesystem. Because the sandbox no longer shares the host kernel, a container-escape or kernel-level exploit inside it hits the hypervisor boundary instead of your host, which is a materially smaller attack surface than namespaces and cgroups. Docker stays the default; Firecracker is opt-in per host.

Selecting the backend is one optional field on the sandbox API (see the REST API reference for the full request shape):

// POST /api/v1/sandboxes — omit `backend` to use the host default (Docker)
const sandbox = await fetch("https://your-temps-instance.com/api/v1/sandboxes", {
  method: "POST",
  headers: { Authorization: `Bearer ${process.env.TEMPS_API_KEY}` },
  body: JSON.stringify({
    image: "python:3.12",
    backend: "firecracker", // kernel-level isolation via KVM
    disk_size_mb: 2048, // Firecracker only; the Docker backend ignores it
  }),
});

Requesting firecracker on a host that cannot run it returns a 400 with the reason, rather than silently downgrading to a container. Isolation is a property you asked for, so Temps refuses to quietly give you less of it.

An operator enables the backend once per host:

# Provisions the pinned Firecracker binary, guest kernel, network, then boots
# a smoke-test VM. The backend stays disabled unless that smoke test passes.
temps firecracker setup

# Probe the host without changing anything
temps firecracker setup --check

What this needs: a Linux host with KVM available at /dev/kvm. That rules out macOS dev machines and most budget VPS plans without nested virtualization, which is exactly why Docker remains the default. Bare metal and nested-virt-enabled cloud instances run it fine, and the choosing a server guide covers the provider-by-provider specifics. The Docker daemon is still required either way, because Temps uses it as the image toolchain: your sandbox image is pulled and exported through Docker, then converted to an ext4 root filesystem and cached per image digest.

Honest scope note: Firecracker support shipped in the 0.1.0 beta line and is opt-in. Two hardening items from the design are deliberately deferred: VMs currently run as the server's own user rather than under Firecracker's jailer (still KVM-isolated, but one layer short of the target), and snapshot-based pause and resume is not implemented yet. If snapshot-and-fork is central to your agent architecture, Morph Cloud still leads on that specific capability.

Cost Comparison: E2B vs Temps at Scale

Pros

• Two isolation levels behind one API: Docker containers or Firecracker microVMs, chosen per sandbox
• Kernel-level KVM isolation without sending code to someone else's cloud
• No per-sandbox billing, just flat VPS cost regardless of execution volume
• Built-in AI Gateway for multi-provider LLM routing with unified cost tracking
• AI Autofixer runs code fixes in sandboxed clones
• Error tracking, session replay, analytics, and uptime monitoring included
• Git-push deployments with preview environments
• Free to self-host under Apache 2.0, or ~$6/mo on Temps Cloud, all from a single binary

Cons

• The Firecracker backend needs a Linux host with /dev/kvm, so macOS and most budget VPS plans can only run the Docker backend
• Firecracker VMs do not yet run under the jailer, and snapshot-based pause and resume is not implemented
• Docker remains a hard dependency even for Firecracker sandboxes, since it is the image toolchain
• No native Jupyter kernel or browser automation support out of the box
• No GPU passthrough on the Firecracker backend, which is a Firecracker limitation rather than a Temps one
• Self-hosting requires basic Linux server management

Best For

Teams running AI agents at scale on self-hosted infrastructure who need sandbox execution + LLM routing + observability without per-execution billing or cloud data residency, including teams that need kernel-level isolation for untrusted code but cannot ship that code to a managed cloud.

3. Modal — Strongest Option for Python ML Workloads

Modal is a serverless cloud platform for running Python functions at scale. While not a sandbox SDK in the E2B sense, Modal is widely used as an AI execution environment because it provisions GPU compute on demand, boots in seconds, and bills per millisecond of actual CPU/GPU use.

Citation Capsule: Modal provides serverless GPU and CPU compute for Python functions, with sub-second cold starts and billing per millisecond. It supports CUDA, common ML libraries, and custom container images. New accounts receive $30 in free credits.

Why Modal as an E2B Alternative?

Modal shines for Python-heavy AI workloads — fine-tuning, inference, batch embedding, and agent pipelines that need GPU access. You define your function, specify the container environment, and Modal handles provisioning:

import modal

app = modal.App("my-agent")

@app.function(image=modal.Image.debian_slim().pip_install("anthropic"))
def run_agent(prompt: str) -> str:
    import anthropic
    client = anthropic.Anthropic()
    message = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        messages=[{"role": "user", "content": prompt}],
    )
    return message.content[0].text

Pros

• On-demand GPU access (A10, A100, H100)
• Sub-second cold starts for CPU functions
• Per-millisecond billing — true pay-per-use
• Strong Python and CUDA ecosystem support
• Custom Docker images supported
• $30 free credit for new accounts

Cons

• Python-only (no multi-language sandbox like E2B)
• No interactive REPL or live file system access
• All execution runs in Modal's cloud
• No built-in AI Gateway or LLM routing
• Billing surprises possible with long-running GPU jobs

Best For

AI teams running Python ML workloads — fine-tuning, batch inference, embedding pipelines — that need GPU access without managing GPU servers.

4. Daytona — Self-Hosted Dev Environment Sandboxes

Daytona is an open-source sandbox runtime for running AI-generated code. Its README describes it as "secure and elastic infrastructure for AI-generated code execution and agent workflows", built on OCI/Docker compatibility, with sandboxes it claims spin up in under 90 ms.

Read this before choosing it: on 11 June 2026 Daytona moved core development to a private codebase. The public daytonaio/daytona repository now carries a notice that it "is no longer maintained" and "will receive no further updates, fixes, or releases". It stays usable under its existing licence, which is AGPL-3.0, a strong copyleft network licence with materially different obligations from Apache 2.0. Treat the open-source version as a frozen fork rather than a maintained project.

Why Daytona as an E2B Alternative?

Daytona suits agents that need a persistent, stateful workspace (a cloned repo, installed dependencies, a running dev server) rather than a one-shot execution session. Note that earlier write-ups, including an earlier version of this post, described Daytona as a Dev Containers manager. That was its pre-2025 positioning; it no longer markets Dev Container support.

The Daytona SDK creates and manages workspaces programmatically:

import { Daytona } from "@daytonaio/sdk";

const daytona = new Daytona();
const workspace = await daytona.create({
  language: "typescript",
});

const response = await workspace.process.code_run("console.log('hello from sandbox')");
console.log(response.result);

await daytona.remove(workspace);

Pros

• Open source and self-hostable
• Fast sandbox creation (Daytona advertises under 90 ms, faster than E2B's stated sub-200 ms)
• Persistent, stateful workspaces for agent navigation
• No per-execution billing

Cons

• The open-source repository is unmaintained as of June 2026, with development moved to a private codebase
• AGPL-3.0, not a permissive licence; the copyleft terms matter if you embed it in a product
• No built-in AI Gateway, error tracking, or deployment platform
• Smaller ecosystem and community than E2B

Best For

Teams that want a self-hosted sandbox runtime and are comfortable running a frozen AGPL codebase, or who intend to use Daytona's managed offering rather than the public repo.

5. Morph Cloud — Snapshotting and Forking Agent State

Morph Cloud is a Firecracker-based sandbox platform built specifically for AI agent workflows. Its standout feature is snapshot and resume: you can pause a running sandbox mid-execution, save its full state (memory, filesystem, network connections), and fork it into parallel branches for exploring multiple agent trajectories simultaneously.

Citation Capsule: Morph Cloud provides Firecracker microVM sandboxes with snapshot, fork, and resume capabilities — allowing AI agent workflows to branch at any execution point without restarting. This enables parallel agent exploration and caching of expensive setup steps.

Why Morph Cloud as an E2B Alternative?

For agent workflows that explore multiple paths from a common starting point — evaluating several code fixes, exploring different prompt strategies, running tree-of-thought reasoning — Morph Cloud's snapshot and fork model eliminates redundant setup. Instead of booting a new sandbox for each branch, you snapshot after the setup phase and fork from there.

import morphcloud

client = morphcloud.MorphCloudClient()

# Boot and set up the sandbox once
sandbox = client.sandboxes.create(vcpus=2, memory=512, disk_size=700)
sandbox.exec("pip install -r requirements.txt")

# Snapshot after setup
snapshot = sandbox.snapshot()

# Fork into parallel branches for different fixes
branch_a = snapshot.fork()
branch_b = snapshot.fork()

# Run different agent strategies in parallel
result_a = branch_a.exec("python fix_strategy_a.py")
result_b = branch_b.exec("python fix_strategy_b.py")

Pros

• Snapshot and fork — branch agent state at any execution point
• Firecracker microVMs — genuine kernel-level isolation
• Built for AI agent parallel exploration workflows
• Reduces redundant setup computation across branches

Cons

• Managed cloud only — no self-hosting option
• Usage-based billing in MCU-hours (1 MCU = 1 vCPU-hour + 4 GB RAM-hours + 16 GB disk-hours, $0.05/MCU standard)
• Smaller ecosystem and community than E2B
• No AI Gateway, error tracking, or deployment platform
• Limited language and framework integrations compared to E2B

Best For

AI agent workflows that explore multiple solution paths in parallel from a shared starting state — tree-of-thought reasoning, parallel code fix evaluation, A/B prompt testing.

6. Raw Firecracker or gVisor — For Teams That Need True Kernel Isolation

Some teams building multi-tenant AI platforms, where users submit arbitrary code for execution, require kernel-level isolation on infrastructure they control. Building directly on Firecracker (AWS's open-source microVM technology) or gVisor (Google's user-space kernel) is the deepest option.

Worth checking before you commit to it: the main reason to build this yourself used to be that no self-hosted platform gave you Firecracker with a usable API on top. Temps now ships exactly that, so the DIY path is mainly for teams whose requirements fall outside what its backend does, such as snapshot-and-fork, custom device models, or a non-KVM hypervisor.

Citation Capsule: Firecracker is AWS's open-source microVM VMM, written in Rust, originally built to power Lambda and Fargate. gVisor is Google's open-source user-space kernel that intercepts system calls. Both provide isolation beyond Linux namespaces and cgroups.

Why Raw Firecracker/gVisor?

• Firecracker: Sub-200 ms boot, minimal memory overhead (~5 MB per VM), purpose-built for serverless. Powers E2B and Morph Cloud's sandbox layer.
• gVisor: Does not require hardware virtualization. Intercepts system calls in user space, making it suitable for environments where nested virtualization is unavailable.

Both require significant engineering investment: VM networking, image management, lifecycle management, and security hardening. This is the DIY path — you build the sandbox layer that E2B sells as a managed API.

Pros

• Full kernel-level isolation — appropriate for untrusted multi-tenant code
• No vendor dependency — you own the stack
• Maximum control over performance, networking, and security policy

Cons

• Months of engineering investment before you have a production-ready sandbox
• Ongoing maintenance burden for security patches and image management
• No managed API — you build the SDK and client libraries
• Not cost-effective unless running at very high scale with specific isolation requirements

Best For

Teams building multi-tenant AI platforms where users submit arbitrary code, and where container-level isolation (Docker) is insufficient for the threat model.

How Do E2B Alternative Costs Compare at Scale?

For a production AI agent running 1,000 tasks per day, each consuming 10 minutes of sandbox compute:

The self-hosted options (Temps, Daytona, DIY) win on cost at scale. The managed options (E2B, Modal, Morph Cloud) win on zero operational overhead. The isolation spectrum runs from Docker (process-level) through gVisor (user-space kernel) to Firecracker (full VM), so choose based on your threat model.

The spectrum used to map cleanly onto the cost table: cheap self-hosted meant process isolation, and kernel isolation meant per-second cloud billing. Temps breaks that pairing by offering both backends on hardware you already pay a flat rate for, provided that hardware exposes KVM.

Frequently Asked Questions

What is the best E2B alternative in 2026?

There is no single best one; it depends on whether you can run your own Linux host. If you want managed sandboxes with no server to operate, E2B remains the strongest general choice, Modal leads for Python ML workloads needing GPUs, and Morph Cloud is the pick if you need to snapshot and fork agent state mid-execution. If per-compute-second billing or data residency rules out a managed cloud, the self-hosted options are Temps and Daytona, where Daytona suits agents that navigate full dev environments and Temps suits teams that also want kernel-level Firecracker isolation, LLM routing, and observability from one binary. (Temps is our product.) The cost crossover is the practical trigger: sandbox compute billed around $545/mo on E2B runs on a ~$6/mo VPS self-hosted.

Does Temps support Firecracker?

Yes. Temps runs Firecracker microVMs as a first-class sandbox backend alongside Docker, selectable per sandbox with an optional backend: "firecracker" field on the sandbox API. Each Firecracker sandbox is a KVM virtual machine with a pinned guest kernel and its own root filesystem, giving the same class of kernel-level isolation E2B and Morph Cloud provide, on hardware you own. An operator enables it once per host with temps firecracker setup, which provisions the pinned Firecracker binary, guest kernel, and network, then boots a smoke-test VM before the backend is marked available. Docker remains the default backend.

What do I need to run Firecracker sandboxes on Temps?

A Linux host with KVM exposed at /dev/kvm. Bare-metal servers and cloud instances with nested virtualization enabled qualify; macOS machines and most budget VPS plans do not, and those hosts fall back to the Docker backend. Docker itself is still required on Firecracker hosts because Temps uses it to pull and export sandbox images before converting them to the ext4 root filesystem each microVM boots from. Requesting the Firecracker backend on a host without KVM returns a descriptive error instead of silently downgrading to a container.

What is E2B used for?

E2B provides sandboxed code execution environments for AI agents. Its primary use case is running LLM-generated code safely — an AI agent writes Python or JavaScript, E2B executes it in an isolated Firecracker microVM, and returns stdout, stderr, and any generated files. It is used in AI coding assistants, autonomous agents, and data analysis pipelines where the AI needs to run code as part of its reasoning process.

Does Temps support Jupyter notebooks in sandboxes?

Temps' container execution model supports running Jupyter kernels inside containers, but it does not provide a Jupyter-specific SDK like E2B's Code Interpreter. Teams using Temps for agent workflows typically exec Python scripts directly rather than through a Jupyter kernel. If native Jupyter kernel execution is a hard requirement, E2B's Code Interpreter SDK is the better fit.

Is Docker isolation sufficient for AI sandboxes?

It depends on your threat model. Docker isolation (Linux namespaces + cgroups) is sufficient for first-party AI agents running your code, your prompts, your execution. For multi-tenant platforms where end users submit arbitrary code, Docker isolation has a larger attack surface than Firecracker microVMs or gVisor, because every container shares the host kernel. The majority of AI coding agent deployments run first-party code and are well-served by Docker-level isolation. On Temps this is no longer an either/or decision: run the Docker backend for trusted first-party agents and switch specific sandboxes to the Firecracker backend when you need a hardware-virtualized boundary for untrusted code. The security overview sets out the wider platform threat model.

How does Temps' AI Gateway compare to E2B?

E2B does not include an AI Gateway. Temps' built-in AI Gateway proxies OpenAI, Anthropic, Gemini, and Grok through a single OpenAI-compatible endpoint, with unified cost tracking, request logging, and rate limiting, replacing tools like Helicone or LiteLLM. For teams running AI agents that call multiple LLM providers, Temps' bundled gateway eliminates a separate SaaS dependency. We wrote up why the gateway belongs in the platform and how to build multi-provider LLM routing separately.

Can I self-host E2B?

Yes. E2B publishes an official self-hosting guide for Google Cloud and AWS, and both e2b-dev/E2B and e2b-dev/infra are Apache-2.0 licensed. An earlier version of this post said otherwise, which was wrong.

The real distinction is what self-hosting costs you to operate. E2B's path provisions a cluster: Packer, Terraform and Nomad, with the SDK pointed at your own domain. That is a reasonable trade if you want E2B's SDK on your own infrastructure, but it is a different weight class from installing a single binary. Temps ships its Firecracker backend in the same binary as the rest of the platform, provisioned with temps firecracker setup, and falls back to Docker on hosts without KVM. Pick E2B self-hosted if you want its SDK and can run a Nomad cluster; pick Temps if you want one process to install and observability bundled in.

The Bottom Line

If you are not sure, start with E2B. The free tier is generous enough to prove out an agent, and you will learn what your real execution volume and isolation requirements are before committing to infrastructure. Migrating to a self-hosted sandbox later is a smaller job than picking the wrong architecture now.

Choose E2B if you want kernel-level isolation with zero infrastructure management and a polished SDK that integrates with every major AI framework, and you are comfortable with per-compute-second billing and code execution in E2B's cloud.

Choose Modal if your AI workloads are Python-heavy and need on-demand GPU compute for fine-tuning or inference.

Choose Morph Cloud if your agent architecture explores multiple solution paths in parallel and you need to snapshot and fork sandbox state at mid-execution points. No self-hosted option matches this today, ours included.

Choose Daytona if your agents need persistent stateful workspaces rather than one-shot execution, and you have accepted that the open-source repository is unmaintained as of June 2026 and is AGPL-3.0.

Choose Temps (our platform) if your sandbox bill has outgrown a VPS or compliance rules out a managed cloud, and you want Docker or Firecracker isolation per sandbox with LLM routing and observability in one Apache 2.0 binary. The quickstart covers setup. Skip it if you need a Jupyter-native SDK, GPU sandboxes, or snapshot-and-fork.

Build on Firecracker/gVisor only if your requirements fall outside what a platform-managed microVM backend covers, such as snapshot-and-fork, custom device models, or a non-KVM hypervisor. The engineering cost is high, and it is now avoidable for the common case.

The AI sandbox market is moving fast, and the old tradeoff is breaking down. Kernel-level isolation no longer implies a managed cloud and per-second billing, because self-hosted platforms now run Firecracker directly. The question is no longer just "how isolated is the sandbox?" but "what is the total cost of running agents at production scale, and where does my data live?"